
"""simple docstring"""



import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a       :						Union[str, Any]       		=		1_6
a       :						Union[str, Any]       		=		3_2


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A       ,   _A = 1_6   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = AutoTokenizer.from_pretrained("""bert-base-cased"""   )
    _UpperCAmelCase        = DatasetDict(
        {
            """train""": dataset["""train"""].select(_A   ),
            """validation""": dataset["""train"""].select(_A   ),
            """test""": dataset["""validation"""],
        }   )

    def tokenize_function(_A   ):
        # max_length=None => use the model max length (it's actually the default)
        _UpperCAmelCase        = tokenizer(examples["""sentence1"""]       ,   examples["""sentence2"""]       ,   truncation=_A       ,   max_length=_A   )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        _UpperCAmelCase        = datasets.map(
            _A       ,   batched=_A       ,   remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,   )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _UpperCAmelCase        = tokenized_datasets.rename_column("""label"""       ,   """labels"""   )

    def collate_fn(_A   ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _UpperCAmelCase        = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            _UpperCAmelCase        = 1_6
        elif accelerator.mixed_precision != "no":
            _UpperCAmelCase        = 8
        else:
            _UpperCAmelCase        = None

        return tokenizer.pad(
            _A       ,   padding="""longest"""       ,   max_length=_A       ,   pad_to_multiple_of=_A       ,   return_tensors="""pt"""       ,   )

    # Instantiate dataloaders.
    _UpperCAmelCase        = DataLoader(
        tokenized_datasets["""train"""]       ,   shuffle=_A       ,   collate_fn=_A       ,   batch_size=_A   )
    _UpperCAmelCase        = DataLoader(
        tokenized_datasets["""validation"""]       ,   shuffle=_A       ,   collate_fn=_A       ,   batch_size=_A   )

    _UpperCAmelCase        = DataLoader(
        tokenized_datasets["""test"""]       ,   shuffle=_A       ,   collate_fn=_A       ,   batch_size=_A   )

    return train_dataloader, eval_dataloader, test_dataloader


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    # Download the dataset
    _UpperCAmelCase        = load_dataset("""glue"""       ,   """mrpc"""   )
    # Create our splits
    _UpperCAmelCase        = StratifiedKFold(n_splits=int(args.num_folds   )   )
    # Initialize accelerator
    _UpperCAmelCase        = Accelerator(cpu=args.cpu       ,   mixed_precision=args.mixed_precision   )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _UpperCAmelCase        = config["""lr"""]
    _UpperCAmelCase        = int(config["""num_epochs"""]   )
    _UpperCAmelCase        = int(config["""seed"""]   )
    _UpperCAmelCase        = int(config["""batch_size"""]   )

    _UpperCAmelCase        = evaluate.load("""glue"""       ,   """mrpc"""   )

    # If the batch size is too big we use gradient accumulation
    _UpperCAmelCase        = 1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        _UpperCAmelCase        = batch_size // MAX_GPU_BATCH_SIZE
        _UpperCAmelCase        = MAX_GPU_BATCH_SIZE

    set_seed(_A   )

    # New Code #
    # Create our folds:
    _UpperCAmelCase        = kfold.split(np.zeros(datasets["""train"""].num_rows   )       ,   datasets["""train"""]["""label"""]   )
    _UpperCAmelCase        = []
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(_A   ):
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = get_fold_dataloaders(
            _A       ,   _A       ,   _A       ,   _A       ,   )
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        _UpperCAmelCase        = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       ,   return_dict=_A   )

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        _UpperCAmelCase        = model.to(accelerator.device   )

        # Instantiate optimizer
        _UpperCAmelCase        = AdamW(params=model.parameters()       ,   lr=_A   )

        # Instantiate scheduler
        _UpperCAmelCase        = get_linear_schedule_with_warmup(
            optimizer=_A       ,   num_warmup_steps=1_0_0       ,   num_training_steps=(len(_A   ) * num_epochs) // gradient_accumulation_steps       ,   )

        # Prepare everything
        # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
        # prepare method.
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = accelerator.prepare(
            _A       ,   _A       ,   _A       ,   _A       ,   _A   )

        # Now we train the model
        for epoch in range(_A   ):
            model.train()
            for step, batch in enumerate(_A   ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device   )
                _UpperCAmelCase        = model(**_A   )
                _UpperCAmelCase        = outputs.loss
                _UpperCAmelCase        = loss / gradient_accumulation_steps
                accelerator.backward(_A   )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(_A   ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device   )
                with torch.no_grad():
                    _UpperCAmelCase        = model(**_A   )
                _UpperCAmelCase        = outputs.logits.argmax(dim=-1   )
                _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
                metric.add_batch(
                    predictions=_A       ,   references=_A       ,   )

            _UpperCAmelCase        = metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""       ,   _A   )

        # New Code #
        # We also run predictions on the test set at the very end
        _UpperCAmelCase        = []
        for step, batch in enumerate(_A   ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device   )
            with torch.no_grad():
                _UpperCAmelCase        = model(**_A   )
            _UpperCAmelCase        = outputs.logits
            _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
            fold_predictions.append(predictions.cpu()   )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()   )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(_A       ,   dim=0   )   )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    _UpperCAmelCase        = torch.cat(_A       ,   dim=0   )
    _UpperCAmelCase        = torch.stack(_A       ,   dim=0   ).sum(dim=0   ).div(int(args.num_folds   )   ).argmax(dim=-1   )
    _UpperCAmelCase        = metric.compute(predictions=_A       ,   references=_A   )
    accelerator.print("""Average test metrics from all folds:"""       ,   _A   )


def 			_UpperCamelCase						(    )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = argparse.ArgumentParser(description="""Simple example of training script."""   )
    parser.add_argument(
        """--mixed_precision"""       ,   type=_A       ,   default=_A       ,   choices=["""no""", """fp16""", """bf16""", """fp8"""]       ,   help="""Whether to use mixed precision. Choose"""
        """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
        """and an Nvidia Ampere GPU."""       ,   )
    parser.add_argument("""--cpu"""       ,   action="""store_true"""       ,   help="""If passed, will train on the CPU."""   )
    # New Code #
    parser.add_argument("""--num_folds"""       ,   type=_A       ,   default=3       ,   help="""The number of splits to perform across the dataset"""   )
    _UpperCAmelCase        = parser.parse_args()
    _UpperCAmelCase        = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
    training_function(_A       ,   _A   )


if __name__ == "__main__":
 main()

"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step